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"This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area."

"This book constitutes the refereed proceedings of the 5th INNS IAPR TC3 GIRPR International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised full papers presented were carefully reviewed and selected for inclusion in this volume. They cover a large range of topics in the field of neural network- and machine learning-based pattern recognition presenting and discussing the latest research, results, and ideas in these areas."

"Skripsi ini dilakukan sebagai penelitian untuk menganalisa proses identifikasi retina mata menggunakan metode neural network berbasis perangkat pemrograman komputasi numerik, yakni suatu sistem sederhana yang dapat menangani serangkaian proses pengolahan dan pelatihan menggunakan sumber informasi awal dari citra biometrik khususnya retina mata untuk bisa dijadikan sebagai identitas pribadi yang unik. Pada pengolahan citra retina mata manusia ini meliputi dua tahap yaitu tahap pra-pengolahan dan tahap identifikasi menggunakan neural network. Pada tahap pra pengolahan, proses yang pertama kali dilakukan adalah pengubahan ukuran citra, hal ini dilakukan untuk mempermudah proses pengolahan berikutnya dalam mencari pola unik pada citra tersebut. Proses kedua adalah memusatkan perhatian pada daerah citra yang dianggap unik dengan mencuplik citra pada suatu area yang dianggap unik tersebut yaitu bagian syaraf optik. Kemudian dilakukan beberapa pengolahan lanjutan untuk memperoleh citra syaraf optik yang lebih spesifik yang digunakan sebagai masukan data pelatihan pada neural network. Pada tahap ini diharapkan sistem dapat bekerja dengan baik dalam mengidentifikasi retina mata manusia.

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Method base on the peripheral numeric computation program, a simple system that able to handle connecting structure of processing and training use the information source of image biometric especially retina to be able as unique personal identity. At processing of human retina image cover two phases that is pre-process and identify by neural network.

At pre-processing phase, the first process is image resize this matter is conducted to alleviate the next process in searching unique pattern of the image. The second process is give all mind at image area that assumed unique by crop image at one particular area that is optic nerve. Then conduct some processing to obtain more specific optic nerve image which is used as input of neural network training data. At this phase this system is expected work well in identifying retina of human eye."

"Secara teoritis, biometrik dapat digunakan untuk mengidentifikasi dan memverifikasi suatu individu. Iris mata merupakan salah satu instrumen biometric yang handal, karena keunikan dari dan kompleksitasnya.Di dalam penelitian ini dirancang bangun program identifikasi iris mata menggunakan metode Jaringan Syaraf Tiruan (JST). Citra mata dijital yang akan diidentifikasi pertama-tama dilakukan pra-pengolahan terlebih dahulu. Proses ini memisahkan bagian iris dari citra mata menggunakan metode morphologi, yaitu close, erosi dan dilasi. Selanjutnya, citra disegmentasi untuk memisahkan citra iris berbentuk lingkaran dalam koordinat x-y menjadi format polar r-θ berbentuk persegi panjang. Citra polar kemudian diekstrasi untuk mendapatkan nilai karakteristik rata-ratanya dalam bentuk matriks 40 x 1. Nilai karakteristik dilatih dan dimasukkan ke dalam database sebagai input pembanding untuk proses identifikasi. JST terdiri dari 10 layer tersembunyi, 1 layer keluaran, dengan fungsi aktifasi tansig dan purelin.Setelah dilakukan pelatihan untuk 80 citra iris, baik mata kiri maupun kanan, proses identifikasi mencapai tingkat akurasi rata-rata sebesar 87% untuk 5 buah input citra dengan 20 kali uji coba.

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Theorically, biometric can be used to identify dan verify an individu. Iris is one of biometric identifier that highly acceptable because of its uniqueness and complexity.

The objective of this research is to identifiy an iris using Artificial Neural Network (ANN) method. First, the digital infrared image of eye will be preprocessed which separate the iris from the eye using morphology technique, such as closing, erosion, and dilation. The iris is then transformed from x-y dimension into r-θ polar image, which convert the circle shape into rectangle one. The image was then extracted in order to get the average value of its intensities and saved in 40 x 1 matrix size. These values will be trained in the ANN and inserted into a database to be used as a comparator in identification process. The ANN consisted of 10 hidden layer, 1 output layer, and activation functions of tansig and purelin, respectively.

Using 80 images as training data, the identification accuracy reached 87 % for 5 images and 20 times of test for left side and right side eyes."